CN111681113A - System and server for fund product object configuration - Google Patents
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Abstract
The embodiment of the application provides a system and a server for fund product object configuration, wherein the system comprises: the server is configured to: after the revenue data is obtained, determining exposure parameters of each fund product object under a plurality of preselected market indexes according to the revenue data; determining investment style attributes corresponding to the exposure parameters for each fund product object; determining a candidate fund product object corresponding to the investment style attribute matched with the investment target of the user; generating time-selecting stock-selecting capability parameters corresponding to the candidate fund product objects; selecting a target fund product object from the candidate fund product objects according to the time-to-select stock-selecting capability parameter, and generating configuration data for the target fund product object. By the method and the system, the fund product is optimally configured, the investment style and the time-selecting and stock-selecting capacity of the fund can be considered, and the overall performance of fund investment is guaranteed.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a system and a server for fund product object configuration.
Background
The entrusted direct casting is to directly arrange pension products which are issued by an enterprise annuity fund investment manager and pass through records of human resources and the social security department on a planning level by a legal person entrusted organization. The entrusted direct-casting business is developed, the production investment is realized, the administrator can be encouraged to concentrate on the product investment management, the entrusted person is endowed with the rights of actively investing and configuring pension products, the investment level of pension investment work is reduced, and the investment efficiency is improved.
At present, stock-type pension products on the market are numerous and have different quality, and the market public disclosure data is less, and the intensive quantitative analysis of pension products is lacked by effective analysis technology and means, so that the pension products actually configured by trustees and administrative staff are mostly more prone to being configured with pension products issued by the trustees and other administrative staff, the pension products issued by the trustees are rarely actively configured, the pension products meeting the investment concept of the trustees and the configuration strategy of a large class of assets are difficult to select, more excellent investment managers are difficult to select, and the performance of trusteely direct investment is difficult to be steadily improved.
Disclosure of Invention
In view of the above, a system and server are proposed to provide a fund product object configuration that overcomes or at least partially addresses the above problems, comprising:
a system for fund product object configuration, the system comprising an acquisition device, a server, and an interaction device, comprising:
the acquisition device is configured to:
acquiring income data corresponding to a plurality of fund product objects;
the server is configured to:
after the revenue data is obtained, determining exposure parameters of each fund product object under a plurality of preselected market indexes according to the revenue data;
determining investment style attributes corresponding to the exposure parameters for each fund product object;
determining a candidate fund product object corresponding to the investment style attribute matched with the investment target of the user;
generating a time-selecting stock-selecting capability parameter corresponding to the candidate fund product object;
selecting a target fund product object from the candidate fund product objects according to the time-selecting stock-selecting capability parameter, and generating configuration data aiming at the target fund product object;
the interaction device is configured to:
and displaying the configuration data, and responding to user operation and adopting the configuration data to perform configuration.
Optionally, said determining exposure parameters of each fund product object respectively at a preselected plurality of market indices according to said revenue data comprises:
generating an actual daily yield vector and a risk-free daily yield vector corresponding to the yield data for each fund product object;
and calculating the exposure parameters corresponding to the actual daily rate of return vector and the risk-free daily rate of return vector by adopting a regression equation aiming at each preselected market index.
Optionally, for each preselected market index, calculating the exposure parameters corresponding to the actual daily rate of return vector and the risk-free daily rate of return vector using a regression equation, including:
performing rolling regression on the actual daily yield vector and the risk-free daily yield vector by adopting a regression equation aiming at each preselected market index to obtain a plurality of rolling regression results;
and smoothing the plurality of rolling regression results to obtain an integral regression result which is used as an exposure parameter corresponding to the market index.
Optionally, the determining, for each fund product object, the investment style attribute corresponding to the exposure parameter includes:
screening a target market index from the plurality of market indexes according to the exposure parameter;
and determining the investment style attribute corresponding to the target market index.
Optionally, the generating of the time-selected stock-selecting capability parameter corresponding to the candidate fund product object includes:
inputting the income data corresponding to the candidate fund product object into a preset prediction model;
and receiving a time-selecting stock capacity parameter output by the prediction model.
Optionally, the predictive model comprises a TM-FF3 model.
Optionally, the revenue data includes actual daily revenue rate, and the fund product object is a stock-type pension product object.
A server for fund product object configuration, comprising:
the exposure parameter determining module is used for acquiring income data corresponding to a plurality of fund product objects and determining exposure parameters of each fund product object under a plurality of preselected market indexes according to the income data;
the investment style attribute determining module is used for determining the investment style attribute corresponding to the exposure parameter aiming at each fund product object;
the candidate fund product object determining module is used for determining a candidate fund product object corresponding to the investment style attribute matched with the investment target of the user;
the time-selecting stock-selecting capability parameter generating module is used for generating time-selecting stock-selecting capability parameters corresponding to the candidate fund product objects;
and the configuration data generation module is used for selecting a target fund product object from the candidate fund product objects according to the time-selecting stock-selecting capacity parameter and generating configuration data aiming at the target fund product object.
Optionally, the exposure parameter determination module comprises:
the daily yield vector determination submodule is used for generating an actual daily yield vector and a risk-free daily yield vector corresponding to the yield data for each fund product object;
and the exposure parameter calculation submodule is used for calculating the exposure parameters corresponding to the actual daily rate of return vector and the risk-free daily rate of return vector by adopting a regression equation according to each preselected market index.
Optionally, the exposure parameter calculation sub-module includes:
a rolling regression result obtaining submodule for performing rolling regression on the actual daily yield vector and the risk-free daily yield vector by using a regression equation for each preselected market index to obtain a plurality of rolling regression results;
and the integral regression result obtaining submodule is used for carrying out smoothing treatment on the plurality of rolling regression results to obtain an integral regression result which is used as the exposure parameter corresponding to the market index.
The embodiment of the application has the following advantages:
in the embodiment of the application, the exposure parameters of each fund product object under a plurality of preselected market indexes are determined by collecting the income data corresponding to a plurality of fund product objects and according to the income data, the investment style attribute corresponding to the exposure parameters is determined aiming at each fund product object, then determining candidate fund product objects corresponding to the investment style attributes matched with the investment targets of the users, generating time-selecting stock-selecting capacity parameters corresponding to the candidate fund product objects, selecting a target fund product object from the candidate fund product objects according to the time-selection stock-selection capacity parameter, and configuration data aiming at the target fund product object is generated, and the configuration data is adopted for configuration in response to user operation, so that the optimal configuration of the fund product is realized, the investment style and the time-selecting stock-selecting capability of the fund can be considered, and the overall performance of fund investment is guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the present application will be briefly introduced below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a schematic block diagram of a system for configuring a fund product object according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating steps at a server side according to an embodiment of the present application;
FIG. 3 is a statistical plot of an exposure parameter provided by an embodiment of the present application;
FIG. 4 is a flow chart of another server-side process provided by an embodiment of the present application;
FIG. 5 is a flow chart of another server-side process provided by an embodiment of the present application;
fig. 6 is a schematic structural diagram of a server configured by a fund product object according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, a schematic structural diagram of a system for configuring a fund product object according to an embodiment of the present application is shown, where the system may include an acquisition device 101, a server 102, and an interaction device 103.
Wherein the fund product object may be a stock-type pension product object.
The acquisition device 101 may be configured to:
acquiring income data corresponding to a plurality of fund product objects;
the server 102 may be configured to:
after the revenue data is obtained, determining exposure parameters of each fund product object under a plurality of preselected market indexes according to the revenue data;
determining investment style attributes corresponding to the exposure parameters for each fund product object;
determining a candidate fund product object corresponding to the investment style attribute matched with the investment target of the user;
generating a time-selecting stock-selecting capability parameter corresponding to the candidate fund product object;
selecting a target fund product object from the candidate fund product objects according to the time-selecting stock-selecting capability parameter, and generating configuration data aiming at the target fund product object;
the interaction means 103 may be configured to:
and displaying the configuration data, and responding to user operation and adopting the configuration data to perform configuration.
In the embodiment of the application, by collecting income data corresponding to a plurality of fund product objects, determining exposure parameters of each fund product object under a plurality of preselected market indexes respectively according to the income data, determining investment style attributes corresponding to the exposure parameters aiming at each fund product object, then determining candidate fund product objects corresponding to the investment style attributes matched with the investment goals of users, generating time-selecting stock-selecting capacity parameters corresponding to the candidate fund product objects, selecting target fund product objects from the candidate fund product objects according to the time-selecting stock-selecting capacity parameters, generating configuration data aiming at the target fund product objects, displaying the configuration data, responding to user operation, adopting the configuration data for configuration, realizing the optimized configuration of fund products, and being capable of considering both the investment style and the time-selecting stock-selecting capacity of funds, the overall performance of fund investment is guaranteed.
The server 102 side is described in detail below with reference to fig. 2:
specifically, the method can comprise the following steps:
the income data can be income data in a preselected time interval, and the income data can comprise actual daily income rate and also can comprise data such as net worth corresponding to fund product objects.
In practice, a plurality of market indices, such as those shown in table 1 below, may be pre-selected, and different market indices may characterize different investment styles.
After the fund product objects in the fund product object library are preliminarily screened, a plurality of fund product objects can be obtained, and screening can be specifically carried out according to whether the establishment time corresponding to the fund product covers a preselected time interval required by calculation and whether the fluctuation rate is seriously lower than that of other fund product objects.
After determining the plurality of fund product objects, revenue data within a preselected time interval may be obtained from the open market information or the self-established information base, and then exposure parameters for each respective market index for each fund product object may be determined based on the revenue data to characterize the bias in different investment styles.
TABLE 1
for each fund product object, after the exposure parameters under a plurality of market indexes are obtained, a plurality of exposure parameters are analyzed, and then the investment style attribute corresponding to the fund product object is determined.
In an embodiment of the present application, step 202 may include the following sub-steps:
screening a target market index from the plurality of market indexes according to the exposure parameter; and determining the investment style attribute corresponding to the target market index.
In practical applications, the sum of the exposure parameters at the plurality of market indexes may be set to be a fixed value, such as 1 or 100, and then the largest exposure parameter may be selected from the plurality of exposure parameters, and the market index corresponding to the largest exposure parameter is determined to be the target market index.
After the target market index is determined, the investment style attribute corresponding to the target market index may be determined to be the investment style attribute of the fund product object.
For example, there are several market indices such as big-disk growth, big-disk value, medium-disk value, etc., as shown in fig. 2, the exposure parameter of a fund product object under the big-disk growth is 46.75%, the exposure parameter under the big-disk value is 26.64%, and the exposure parameter under the medium-disk value is 26.61%, the target market index can be determined as big-disk growth, that is, the investment style attribute of the fund product object can be determined as big-disk growth.
when investment entrustment is carried out, an investment target of a user can be determined, so that an investment style attribute matched with the investment target of the user can be screened out, a fund product object corresponding to the investment style attribute is obtained to be used as a candidate fund product object, and the candidate fund product object can be added into an alternative pool.
In one example, candidate pools corresponding to different investment style attributes, such as a large-disk growth candidate pool, a small-disk value growth candidate pool, and the like, may be established, and then the candidate fund product objects may be placed in different candidate pools, and the candidate pools may be periodically updated, new candidate fund product objects may be added, or the original candidate fund product objects may be deleted.
the time-selecting stock-selecting capability parameter may include a time-selecting capability parameter and a stock-selecting capability parameter.
After the candidate fund product object is determined, the time-selection stock-selection capacity analysis can be performed on the candidate fund product object to generate time-selection stock-selection capacity parameters corresponding to the candidate fund product object.
Step 205, selecting a target fund product object from the candidate fund product objects according to the time-selection stock-selection capability parameter, and generating configuration data aiming at the target fund product object.
After the time-of-selection stock-selecting capability parameter is determined, a target fund product object can be selected from the candidate fund product objects according to the time-of-selection stock-selecting capability parameter, and then configuration data for the target fund product object is generated to configure an investment strategy for the target fund product for the user.
In the embodiment of the application, the optimized allocation of the fund products is realized by acquiring the income data corresponding to a plurality of fund product objects, determining the exposure parameters of each fund product object under a plurality of preselected market indexes according to the income data, determining the investment style attributes corresponding to the exposure parameters aiming at each fund product object, then determining the candidate fund product objects corresponding to the investment style attributes matched with the investment targets of the users, generating the optional-time stock-selecting capacity parameters corresponding to the candidate fund product objects, selecting the target fund product object from the candidate fund product objects according to the optional-time stock-selecting capacity parameters, and generating the allocation data aiming at the target fund product object, so that the investment style and the optional-time stock-selecting capacity of the fund can be considered at the same time, and the overall performance of the fund investment is guaranteed.
The server 102 side is described in detail below with reference to fig. 4:
the income data can be income data in a preselected time interval, and the income data can comprise actual daily income rate and also can comprise data such as net worth corresponding to fund product objects.
After the fund product objects in the fund product object library are preliminarily screened, a plurality of fund product objects can be obtained, specifically, screening can be carried out according to whether the corresponding establishment time of the fund product covers a preselection time interval required by calculation and whether the fluctuation rate is seriously lower than that of other fund product objects, and then income data in the preselection time interval can be obtained from public market information or a self-established information base
Step 402, generating an actual daily profitability vector and a risk-free daily profitability vector corresponding to the profitability data for each fund product object;
for each fund product object, the actual daily profitability in the profitability data can be employed to generate an actual daily profitability vector, and a risk-free daily profitability vector can be generated.
Step 403, calculating exposure parameters corresponding to the actual daily rate of return vector and the risk-free daily rate of return vector by using a regression equation for each preselected market index;
for each pre-selected market index, after obtaining the actual daily yield vector and the risk-free daily yield vector, calculating the exposure parameters corresponding to the actual daily yield vector and the risk-free daily yield vector by using a regression equation through a preset quantitative analysis model, wherein the formula is as follows:
R-rf＝β1(R1-rf)+β2(R2-rf)+...+α
wherein β is the exposure parameter, α is the attribution coefficient, rfFor risk-free daily yield vector, RiActual daily rate of return vector R for n daysi＝[ri，1ri，2...ri，(n-1)ri，n]T，ri，1The actual daily profitability on day 1, and so on.
In an embodiment of the present application, step 403 may include the following sub-steps:
performing rolling regression on the actual daily yield vector and the risk-free daily yield vector by adopting a regression equation aiming at each preselected market index to obtain a plurality of rolling regression results; and smoothing the plurality of rolling regression results to obtain an integral regression result which is used as an exposure parameter corresponding to the market index.
In a specific implementation, a time window may be set by using a rolling regression method, and then rolling regression may be performed on the time window to obtain a plurality of rolling regression results, where the rolling regression results may be exposure parameters corresponding to each time window.
As shown in table 2 below, when data from 2014-09-26 are used as a research target, the market index is set to be the small plate value, the small plate growth, the medium plate value, the medium plate growth, the large plate value and the large plate growth, and the time window is set to be 4 weeks, the rolling regression result, that is, the exposure parameter in each time window can be determined.
| Value of the small dish | Growth of small disc | Central value | Growth of the central disc | Large disk value | Growth of large disc | |
| 2014/9/30 | 0％ | 0％ | 0％ | 0％ | 100％ | 0％ |
| 2014/10/10 | 0％ | 0％ | 0％ | 0％ | 100％ | 0％ |
| 2014/10/17 | 0％ | 0％ | 0％ | 0％ | 0％ | 100％ |
| 2014/10/24 | 0％ | 0％ | 0％ | 100％ | 0％ | 0％ |
| 2014/10/31 | 0％ | 0％ | 0％ | 94％ | 0％ | 6％ |
| 2014/11/7 | 0％ | 0％ | 21％ | 74％ | 5％ | 0％ |
| 2014/11/14 | 0％ | 0％ | 0％ | 100％ | 0％ | 0％ |
| 2014/11/21 | 0％ | 66％ | 0％ | 34％ | 0％ | 0％ |
| 2014/11/28 | 0％ | 100％ | 0％ | 0％ | 0％ | 0％ |
| 2014/12/5 | 0％ | 100％ | 0％ | 0％ | 0％ | 0％ |
| 2014/12/12 | 0％ | 57％ | 0％ | 0％ | 0％ | 43％ |
| 2014/12/19 | 0％ | 54％ | 0％ | 0％ | 0％ | 46％ |
| 2014/12/26 | 46％ | 0％ | 0％ | 0％ | 1％ | 53％ |
TABLE 2
After obtaining the multiple rolling regression results, smoothing the multiple rolling regression results, ignoring the smaller style abrupt change in the period, smoothing the exposure parameters of each stage to obtain an overall regression result, and further taking the overall regression result as the exposure parameters corresponding to the market index.
for each fund product object, after the exposure parameters under a plurality of market indexes are obtained, a plurality of exposure parameters are analyzed, and then the investment style attribute corresponding to the fund product object is determined.
when investment entrustment is carried out, an investment target of a user can be determined, so that an investment style attribute matched with the investment target of the user can be screened out, a fund product object corresponding to the investment style attribute is obtained to be used as a candidate fund product object, and the candidate fund product object can be added into an alternative pool.
after the candidate fund product object is determined, the time-selection stock-selection capacity analysis can be performed on the candidate fund product object to generate time-selection stock-selection capacity parameters corresponding to the candidate fund product object.
Step 407, selecting a target fund product object from the candidate fund product objects according to the time-to-select stock-selecting capability parameter, and generating configuration data for the target fund product object.
After the time-of-selection stock-selecting capability parameter is determined, a target fund product object can be selected from the candidate fund product objects according to the time-of-selection stock-selecting capability parameter, and then configuration data for the target fund product object is generated to configure an investment strategy for the target fund product for the user.
In the embodiment of the application, the optimal configuration of the fund product is realized by acquiring the income data corresponding to a plurality of fund product objects, generating an actual daily rate of return vector and a no-risk daily rate of return vector corresponding to the income data for each fund product object, calculating exposure parameters corresponding to the actual daily rate of return vector and the no-risk daily rate of return vector by adopting a regression equation for each preselected market index, then determining an investment style attribute corresponding to the exposure parameters for each fund product object, determining candidate fund product objects corresponding to the investment style attributes matched with the investment targets of the users, generating time-selecting and stock-selecting capacity parameters corresponding to the candidate fund product objects, selecting the target fund product objects from the candidate fund product objects according to the time-selecting and stock-selecting capacity parameters, and generating configuration data for the target fund product objects, and a regression analysis means is adopted, so that the accuracy of the investment style analysis is improved, and the overall performance of the fund investment is guaranteed.
The server 102 side is described in detail below with reference to fig. 5:
step 501, obtaining income data corresponding to a plurality of fund product objects, and determining exposure parameters of each fund product object under a plurality of preselected market indexes according to the income data;
after the fund product objects in the fund product object library are preliminarily screened, a plurality of fund product objects can be obtained, and screening can be specifically carried out according to whether the establishment time corresponding to the fund product covers a preselected time interval required by calculation and whether the fluctuation rate is seriously lower than that of other fund product objects.
After determining the plurality of fund product objects, revenue data within a preselected time interval may be obtained from the open market information or the self-established information base, and then exposure parameters for each respective market index for each fund product object may be determined based on the revenue data to characterize the bias in different investment styles.
for each fund product object, after the exposure parameters under a plurality of market indexes are obtained, a plurality of exposure parameters are analyzed, and then the investment style attribute corresponding to the fund product object is determined.
when investment entrustment is carried out, an investment target of a user can be determined, so that an investment style attribute matched with the investment target of the user can be screened out, a fund product object corresponding to the investment style attribute is obtained to be used as a candidate fund product object, and the candidate fund product object can be added into an alternative pool.
as an example, the predictive model may include a TM-FF3 model, which may be a combination of a TM model and a Fama-French three-factor model, that enables time-of-day and stock-of-stock analysis.
After the candidate fund product object is determined, the time-selected stock-selecting capacity analysis can be performed on the candidate fund product object, and then the income data corresponding to the candidate fund product object can be input into a preset prediction model.
the time-selecting stock-selecting capability parameter may include a time-selecting capability parameter and a stock-selecting capability parameter.
In practical applications, the TM-FF3 model can be analyzed using the following formula:
R-rf＝β1(Rm-rf)+β2HML+β3SMB+b(Rm-rf)2+α
wherein α represents the stock-selecting ability parameter, b represents the time-selecting ability parameter, β1Characterizing systematic risk, SMB characterizing the difference between small and large market value stock profitability, HML characterizing the difference between high and low market value stock profitability, β2、β3Characterizing style exposure values on market value and value.
After the processing of the prediction model, the time-selection stock-selection capability output by the prediction model can be received, and as shown in table 3 below, the candidate fund product object can be determined to have the forward stock-selection capability and the forward time-selection capability by analyzing according to the time-selection stock-selection capability parameter output by the TM-FF 3.
| α | 0.000364 |
| b | 0.083031 |
| β1 | 0.693705 |
| β2 | -0.20822 |
| β3 | -0.11496 |
TABLE 3
Step 506, selecting a target fund product object from the candidate fund product objects according to the time-selection stock-selection capacity parameter, and generating configuration data aiming at the target fund product object.
After the time-of-selection stock-selecting capability parameter is determined, a target fund product object can be selected from the candidate fund product objects according to the time-of-selection stock-selecting capability parameter, and then configuration data for the target fund product object is generated to configure an investment strategy for the target fund product for the user.
In the embodiment of the application, the optimized configuration of the fund products is realized by acquiring the income data corresponding to a plurality of fund product objects, determining the exposure parameters of each fund product object under a plurality of preselected market indexes according to the income data, determining the investment style attribute corresponding to the exposure parameters aiming at each fund product object, then determining the candidate fund product object corresponding to the investment style attribute matched with the investment target of a user, inputting the income data corresponding to the candidate fund product object into a preset prediction model, receiving the optional-time stock-selecting capacity parameter output by the prediction model, selecting the target fund product object from the candidate fund product objects according to the optional-time stock-selecting capacity parameter, generating the configuration data aiming at the target fund product object, analyzing the optional-time stock-selecting capacity through the prediction model, seeking the excess brought by the product with better optional-time stock-selecting capacity at the same stock position, the overall performance of fund investment is guaranteed.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.
Referring to fig. 6, a schematic structural diagram of a server configured by a fund product object provided in an embodiment of the present application is shown, and specifically, the server may include the following modules:
an exposure parameter determining module 601, configured to obtain revenue data corresponding to a plurality of fund product objects, and determine, according to the revenue data, an exposure parameter of each fund product object under a plurality of preselected market indexes;
an investment style attribute determining module 602, configured to determine, for each fund product object, an investment style attribute corresponding to the exposure parameter;
a candidate fund product object determining module 603, configured to determine a candidate fund product object corresponding to the investment style attribute matched with the user investment target;
an optional time stock selection capability parameter generating module 604, configured to generate an optional time stock selection capability parameter corresponding to the candidate fund product object;
a configuration data generating module 605, configured to select a target fund product object from the candidate fund product objects according to the time-selection stock-selection capability parameter, and generate configuration data for the target fund product object.
In an embodiment of the present application, the exposure parameter determining module 601 includes:
the daily yield vector determination submodule is used for generating an actual daily yield vector and a risk-free daily yield vector corresponding to the yield data for each fund product object;
and the exposure parameter calculation submodule is used for calculating the exposure parameters corresponding to the actual daily rate of return vector and the risk-free daily rate of return vector by adopting a regression equation according to each preselected market index.
In an embodiment of the present application, the exposure parameter calculation sub-module includes:
a rolling regression result obtaining submodule for performing rolling regression on the actual daily yield vector and the risk-free daily yield vector by using a regression equation for each preselected market index to obtain a plurality of rolling regression results;
and the integral regression result obtaining submodule is used for carrying out smoothing treatment on the plurality of rolling regression results to obtain an integral regression result which is used as the exposure parameter corresponding to the market index.
In an embodiment of the present application, the investment style attribute determining module 602 includes:
a target market index determination submodule for screening a target market index from the plurality of market indexes according to the exposure parameter;
and the investment style attribute determining submodule is used for determining the investment style attribute corresponding to the target market index.
In an embodiment of the present application, the time-selective stock-selecting capability parameter generating module 604 includes:
the model input submodule is used for inputting the income data corresponding to the candidate fund product object into a preset prediction model;
and the model output submodule is used for receiving the time-selecting stock-selecting capability parameter output by the prediction model.
In one embodiment of the present application, the predictive model comprises a TM-FF3 model.
In an embodiment of the present application, the revenue data includes actual daily revenue rate, and the fund product object is a stock-type pension product object.
For the server embodiment, since it is basically similar to the system embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The system and the server for configuring the fund product object are introduced in detail, and specific examples are applied in the system to explain the principle and the implementation manner of the application, and the description of the above embodiments is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A system for fund product object configuration, the system comprising an acquisition device, a server, and an interaction device, the system comprising:
the acquisition device is configured to:
acquiring income data corresponding to a plurality of fund product objects;
the server is configured to:
after the revenue data is obtained, determining exposure parameters of each fund product object under a plurality of preselected market indexes according to the revenue data;
determining investment style attributes corresponding to the exposure parameters for each fund product object;
determining a candidate fund product object corresponding to the investment style attribute matched with the investment target of the user;
generating a time-selecting stock-selecting capability parameter corresponding to the candidate fund product object;
selecting a target fund product object from the candidate fund product objects according to the time-selecting stock-selecting capability parameter, and generating configuration data aiming at the target fund product object;
the interaction device is configured to:
and displaying the configuration data, and responding to user operation and adopting the configuration data to perform configuration.
2. The system of claim 1, wherein said determining exposure parameters for each fund product object at a preselected plurality of market indices based on said revenue data comprises:
generating an actual daily yield vector and a risk-free daily yield vector corresponding to the yield data for each fund product object;
and calculating the exposure parameters corresponding to the actual daily rate of return vector and the risk-free daily rate of return vector by adopting a regression equation aiming at each preselected market index.
3. The system of claim 2, wherein said calculating, for each pre-selected market index, the exposure parameters for the actual daily rate of return vector and the no risk daily rate of return vector using a regression equation comprises:
performing rolling regression on the actual daily yield vector and the risk-free daily yield vector by adopting a regression equation aiming at each preselected market index to obtain a plurality of rolling regression results;
and smoothing the plurality of rolling regression results to obtain an integral regression result which is used as an exposure parameter corresponding to the market index.
4. The system according to claim 1, 2 or 3, wherein said determining, for each fund product object, investment style attributes corresponding to said exposure parameters comprises:
screening a target market index from the plurality of market indexes according to the exposure parameter;
and determining the investment style attribute corresponding to the target market index.
5. The system of claim 4, wherein said generating an on-time stock-selection capability parameter corresponding to said candidate fund product object comprises:
inputting the income data corresponding to the candidate fund product object into a preset prediction model;
and receiving a time-selecting stock capacity parameter output by the prediction model.
6. The system of claim 5, wherein the predictive model comprises a TM-FF3 model.
7. The system of claim 1, wherein the revenue data includes actual daily revenue rates and the fund product objects are stock-type pension product objects.
8. A server for fund product object configuration, comprising:
the exposure parameter determining module is used for acquiring income data corresponding to a plurality of fund product objects and determining exposure parameters of each fund product object under a plurality of preselected market indexes according to the income data;
the investment style attribute determining module is used for determining the investment style attribute corresponding to the exposure parameter aiming at each fund product object;
the candidate fund product object determining module is used for determining a candidate fund product object corresponding to the investment style attribute matched with the investment target of the user;
the time-selecting stock-selecting capability parameter generating module is used for generating time-selecting stock-selecting capability parameters corresponding to the candidate fund product objects;
and the configuration data generation module is used for selecting a target fund product object from the candidate fund product objects according to the time-selecting stock-selecting capacity parameter and generating configuration data aiming at the target fund product object.
9. The server of claim 8, wherein the exposure parameter determination module comprises:
the daily yield vector determination submodule is used for generating an actual daily yield vector and a risk-free daily yield vector corresponding to the yield data for each fund product object;
and the exposure parameter calculation submodule is used for calculating the exposure parameters corresponding to the actual daily rate of return vector and the risk-free daily rate of return vector by adopting a regression equation according to each preselected market index.
10. The server according to claim 9, wherein the exposure parameter calculation submodule includes:
a rolling regression result obtaining submodule for performing rolling regression on the actual daily yield vector and the risk-free daily yield vector by using a regression equation for each preselected market index to obtain a plurality of rolling regression results;
and the integral regression result obtaining submodule is used for carrying out smoothing treatment on the plurality of rolling regression results to obtain an integral regression result which is used as the exposure parameter corresponding to the market index.
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